Overview

Brought to you by YData

Dataset statistics

Number of variables22
Number of observations223
Missing cells9
Missing cells (%)0.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory37.7 KiB
Average record size in memory173.2 B

Variable types

Text2
Numeric8
DateTime4
Categorical6
Unsupported2

Alerts

Buy Hour is highly overall correlated with Buy IntervalHigh correlation
Buy Interval is highly overall correlated with Buy HourHigh correlation
Buy price is highly overall correlated with Buy value and 4 other fieldsHigh correlation
Buy value is highly overall correlated with Buy price and 3 other fieldsHigh correlation
Industry is highly overall correlated with Buy price and 3 other fieldsHigh correlation
Quantity is highly overall correlated with Buy value and 1 other fieldsHigh correlation
Realised P&L is highly overall correlated with RemarkHigh correlation
Remark is highly overall correlated with Realised P&LHigh correlation
Sector is highly overall correlated with Industry and 1 other fieldsHigh correlation
Sell Hour is highly overall correlated with Sell IntervalHigh correlation
Sell Interval is highly overall correlated with Sell HourHigh correlation
Sell price is highly overall correlated with Buy price and 4 other fieldsHigh correlation
Sell value is highly overall correlated with Buy price and 3 other fieldsHigh correlation
Symbol is highly overall correlated with Buy price and 3 other fieldsHigh correlation
Buy Interval has 8 (3.6%) missing valuesMissing
Buy Time is an unsupported type, check if it needs cleaning or further analysisUnsupported
Sell Time is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2025-11-08 10:51:10.819963
Analysis finished2025-11-08 10:51:15.900054
Duration5.08 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

Distinct53
Distinct (%)23.8%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
2025-11-08T16:21:16.024558image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length25
Median length23
Mean length20.784753
Min length8

Characters and Unicode

Total characters4635
Distinct characters34
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22 ?
Unique (%)9.9%

Sample

1st rowONE 97 COMMUNICATIONS LTD
2nd rowINDIAN OIL CORP LTD
3rd rowCOAL INDIA LTD
4th rowETERNAL LIMITED
5th rowCOAL INDIA LTD
ValueCountFrequency (%)
ltd118
 
15.7%
limited68
 
9.0%
bank55
 
7.3%
indusind35
 
4.6%
industries31
 
4.1%
india26
 
3.5%
corp22
 
2.9%
reliance21
 
2.8%
coal20
 
2.7%
oil12
 
1.6%
Other values (98)345
45.8%
2025-11-08T16:21:16.238110image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
551
11.9%
I532
11.5%
N394
 
8.5%
D390
 
8.4%
T350
 
7.6%
A349
 
7.5%
L340
 
7.3%
E265
 
5.7%
R208
 
4.5%
S188
 
4.1%
Other values (24)1068
23.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)4635
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
551
11.9%
I532
11.5%
N394
 
8.5%
D390
 
8.4%
T350
 
7.6%
A349
 
7.5%
L340
 
7.3%
E265
 
5.7%
R208
 
4.5%
S188
 
4.1%
Other values (24)1068
23.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4635
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
551
11.9%
I532
11.5%
N394
 
8.5%
D390
 
8.4%
T350
 
7.6%
A349
 
7.5%
L340
 
7.3%
E265
 
5.7%
R208
 
4.5%
S188
 
4.1%
Other values (24)1068
23.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4635
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
551
11.9%
I532
11.5%
N394
 
8.5%
D390
 
8.4%
T350
 
7.6%
A349
 
7.5%
L340
 
7.3%
E265
 
5.7%
R208
 
4.5%
S188
 
4.1%
Other values (24)1068
23.0%

ISIN
Text

Distinct53
Distinct (%)23.8%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
2025-11-08T16:21:16.369985image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters2676
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22 ?
Unique (%)9.9%

Sample

1st rowINE982J01020
2nd rowINE242A01010
3rd rowINE522F01014
4th rowINE758T01015
5th rowINE522F01014
ValueCountFrequency (%)
ine095a0101235
 
15.7%
ine002a0101821
 
9.4%
ine522f0101420
 
9.0%
ine038a0102010
 
4.5%
ine040a010349
 
4.0%
ine775a010359
 
4.0%
ine463a010388
 
3.6%
ine982j010208
 
3.6%
ine213a010297
 
3.1%
ine160a010227
 
3.1%
Other values (43)89
39.9%
2025-11-08T16:21:16.577905image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0627
23.4%
1391
14.6%
E234
 
8.7%
N225
 
8.4%
I225
 
8.4%
2221
 
8.3%
A149
 
5.6%
3107
 
4.0%
593
 
3.5%
982
 
3.1%
Other values (18)322
12.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)2676
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0627
23.4%
1391
14.6%
E234
 
8.7%
N225
 
8.4%
I225
 
8.4%
2221
 
8.3%
A149
 
5.6%
3107
 
4.0%
593
 
3.5%
982
 
3.1%
Other values (18)322
12.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2676
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0627
23.4%
1391
14.6%
E234
 
8.7%
N225
 
8.4%
I225
 
8.4%
2221
 
8.3%
A149
 
5.6%
3107
 
4.0%
593
 
3.5%
982
 
3.1%
Other values (18)322
12.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2676
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0627
23.4%
1391
14.6%
E234
 
8.7%
N225
 
8.4%
I225
 
8.4%
2221
 
8.3%
A149
 
5.6%
3107
 
4.0%
593
 
3.5%
982
 
3.1%
Other values (18)322
12.0%

Quantity
Real number (ℝ)

High correlation 

Distinct25
Distinct (%)11.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.852018
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2025-11-08T16:21:16.644425image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median9
Q310
95-th percentile37.2
Maximum100
Range99
Interquartile range (IQR)6

Descriptive statistics

Standard deviation13.967484
Coefficient of variation (CV)1.2870864
Kurtosis18.741523
Mean10.852018
Median Absolute Deviation (MAD)4
Skewness3.8727477
Sum2420
Variance195.09062
MonotonicityNot monotonic
2025-11-08T16:21:16.712601image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
1063
28.3%
535
15.7%
119
 
8.5%
318
 
8.1%
217
 
7.6%
1511
 
4.9%
411
 
4.9%
2011
 
4.9%
256
 
2.7%
75
 
2.2%
Other values (15)27
12.1%
ValueCountFrequency (%)
119
 
8.5%
217
 
7.6%
318
 
8.1%
411
 
4.9%
535
15.7%
63
 
1.3%
75
 
2.2%
83
 
1.3%
93
 
1.3%
1063
28.3%
ValueCountFrequency (%)
1001
 
0.4%
981
 
0.4%
901
 
0.4%
551
 
0.4%
521
 
0.4%
504
1.8%
402
 
0.9%
381
 
0.4%
302
 
0.9%
256
2.7%
Distinct106
Distinct (%)47.5%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
Minimum2021-11-25 00:00:00
Maximum2025-06-11 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-11-08T16:21:16.800173image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T16:21:17.730353image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Buy price
Real number (ℝ)

High correlation 

Distinct204
Distinct (%)91.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean883.4883
Minimum0
Maximum5466
Zeros2
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2025-11-08T16:21:17.847244image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile46.205
Q1199.2
median644.9
Q31400.3
95-th percentile2949.22
Maximum5466
Range5466
Interquartile range (IQR)1201.1

Descriptive statistics

Standard deviation897.43688
Coefficient of variation (CV)1.0157881
Kurtosis3.0833722
Mean883.4883
Median Absolute Deviation (MAD)456.65
Skewness1.6279947
Sum197017.89
Variance805392.95
MonotonicityNot monotonic
2025-11-08T16:21:17.932081image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
206.614
 
1.8%
2586.13
 
1.3%
02
 
0.9%
69.22
 
0.9%
3002
 
0.9%
842.552
 
0.9%
34.82
 
0.9%
2983.552
 
0.9%
103.452
 
0.9%
972.482
 
0.9%
Other values (194)200
89.7%
ValueCountFrequency (%)
02
0.9%
25.751
0.4%
34.82
0.9%
34.91
0.4%
35.731
0.4%
35.81
0.4%
36.131
0.4%
36.291
0.4%
42.661
0.4%
43.651
0.4%
ValueCountFrequency (%)
54661
0.4%
29841
0.4%
2983.552
0.9%
2976.851
0.4%
2968.071
0.4%
29631
0.4%
2961.91
0.4%
2953.351
0.4%
2952.651
0.4%
2951.651
0.4%

Buy value
Real number (ℝ)

High correlation 

Distinct213
Distinct (%)95.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6498.1119
Minimum0
Maximum32465
Zeros2
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2025-11-08T16:21:18.015102image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile204.639
Q11448.01
median4212.75
Q39742.75
95-th percentile15747
Maximum32465
Range32465
Interquartile range (IQR)8294.74

Descriptive statistics

Standard deviation6816.556
Coefficient of variation (CV)1.0490056
Kurtosis2.9175898
Mean6498.1119
Median Absolute Deviation (MAD)3328.45
Skewness1.6418129
Sum1449079
Variance46465435
MonotonicityNot monotonic
2025-11-08T16:21:18.114646image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5172.22
 
0.9%
4212.752
 
0.9%
02
 
0.9%
6052
 
0.9%
3462
 
0.9%
150002
 
0.9%
139.22
 
0.9%
222452
 
0.9%
2013.52
 
0.9%
5813.62
 
0.9%
Other values (203)203
91.0%
ValueCountFrequency (%)
02
0.9%
72.451
0.4%
108.871
0.4%
139.22
0.9%
178.651
0.4%
183.51
0.4%
190.61
0.4%
195.51
0.4%
196.51
0.4%
204.421
0.4%
ValueCountFrequency (%)
324651
0.4%
29768.51
0.4%
296301
0.4%
29516.51
0.4%
294221
0.4%
29337.61
0.4%
29252.51
0.4%
29172.51
0.4%
222452
0.9%
213001
0.4%
Distinct100
Distinct (%)44.8%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
Minimum2021-11-25 00:00:00
Maximum2025-09-17 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-11-08T16:21:18.231728image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T16:21:18.340271image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Sell price
Real number (ℝ)

High correlation 

Distinct189
Distinct (%)84.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean885.82327
Minimum27.64
Maximum5085.25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2025-11-08T16:21:18.468432image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum27.64
5-th percentile75.12
Q1200.025
median649.5
Q31416
95-th percentile2933.427
Maximum5085.25
Range5057.61
Interquartile range (IQR)1215.975

Descriptive statistics

Standard deviation867.80596
Coefficient of variation (CV)0.97966037
Kurtosis2.5898262
Mean885.82327
Median Absolute Deviation (MAD)462.51
Skewness1.5399956
Sum197538.59
Variance753087.18
MonotonicityNot monotonic
2025-11-08T16:21:18.580902image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
86.854
 
1.8%
2154
 
1.8%
167.34
 
1.8%
192.43
 
1.3%
239.33
 
1.3%
1732.23
 
1.3%
82.252
 
0.9%
255.352
 
0.9%
1036.452
 
0.9%
34.152
 
0.9%
Other values (179)194
87.0%
ValueCountFrequency (%)
27.641
0.4%
34.152
0.9%
35.441
0.4%
35.952
0.9%
36.311
0.4%
41.152
0.9%
42.831
0.4%
72.61
0.4%
74.851
0.4%
77.551
0.4%
ValueCountFrequency (%)
5085.251
0.4%
3007.91
0.4%
2983.31
0.4%
2969.951
0.4%
2956.31
0.4%
2955.051
0.4%
2954.851
0.4%
2951.31
0.4%
2946.21
0.4%
2939.651
0.4%

Sell value
Real number (ℝ)

High correlation 

Distinct212
Distinct (%)95.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6635.1498
Minimum72.6
Maximum32439
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2025-11-08T16:21:18.701282image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum72.6
5-th percentile295.582
Q11434.75
median4411.4
Q39948.375
95-th percentile20987.35
Maximum32439
Range32366.4
Interquartile range (IQR)8513.625

Descriptive statistics

Standard deviation6900.0468
Coefficient of variation (CV)1.0399233
Kurtosis2.6308362
Mean6635.1498
Median Absolute Deviation (MAD)3498.35
Skewness1.6007746
Sum1479638.4
Variance47610646
MonotonicityNot monotonic
2025-11-08T16:21:18.834996image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9623
 
1.3%
836.53
 
1.3%
2152
 
0.9%
868.52
 
0.9%
341.52
 
0.9%
141602
 
0.9%
411.252
 
0.9%
6749.22
 
0.9%
12472.52
 
0.9%
5182.251
 
0.4%
Other values (202)202
90.6%
ValueCountFrequency (%)
72.61
0.4%
123.451
0.4%
141.761
0.4%
143.81
0.4%
163.451
0.4%
179.751
0.4%
183.91
0.4%
186.751
0.4%
191.451
0.4%
2152
0.9%
ValueCountFrequency (%)
324391
0.4%
298331
0.4%
29699.51
0.4%
29396.51
0.4%
29371.31
0.4%
29274.51
0.4%
29140.51
0.4%
291251
0.4%
22125.751
0.4%
220221
0.4%

Realised P&L
Real number (ℝ)

High correlation 

Distinct208
Distinct (%)93.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean137.03785
Minimum-2329.4
Maximum7202
Zeros2
Zeros (%)0.9%
Negative113
Negative (%)50.7%
Memory size3.5 KiB
2025-11-08T16:21:18.928336image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-2329.4
5-th percentile-296.2
Q1-42.235
median-0.6
Q351.15
95-th percentile1106.69
Maximum7202
Range9531.4
Interquartile range (IQR)93.385

Descriptive statistics

Standard deviation788.98855
Coefficient of variation (CV)5.75745
Kurtosis38.482362
Mean137.03785
Median Absolute Deviation (MAD)46.9
Skewness5.2146127
Sum30559.44
Variance622502.93
MonotonicityNot monotonic
2025-11-08T16:21:19.025224image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-333
 
1.3%
18.52
 
0.9%
-20.52
 
0.9%
0.252
 
0.9%
2.252
 
0.9%
-32
 
0.9%
-62
 
0.9%
02
 
0.9%
21.62
 
0.9%
6.52
 
0.9%
Other values (198)202
90.6%
ValueCountFrequency (%)
-2329.41
0.4%
-1163.851
0.4%
-1026.751
0.4%
-10171
0.4%
-8771
0.4%
-456.251
0.4%
-4391
0.4%
-380.751
0.4%
-378.161
0.4%
-3371
0.4%
ValueCountFrequency (%)
72021
0.4%
5196.61
0.4%
35651
0.4%
29701
0.4%
27001
0.4%
2149.51
0.4%
18761
0.4%
1752.81
0.4%
1302.751
0.4%
12151
0.4%

Remark
Categorical

High correlation 

Distinct4
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
Intraday trade
143 
Delivery Trade
70 
New shares credit from IPO
 
8
New shares credit from Bonus
 
2

Length

Max length28
Median length14
Mean length14.556054
Min length14

Characters and Unicode

Total characters3246
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIntraday trade
2nd rowDelivery Trade
3rd rowDelivery Trade
4th rowIntraday trade
5th rowDelivery Trade

Common Values

ValueCountFrequency (%)
Intraday trade143
64.1%
Delivery Trade70
31.4%
New shares credit from IPO8
 
3.6%
New shares credit from Bonus2
 
0.9%

Length

2025-11-08T16:21:19.140953image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-08T16:21:19.225944image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
trade213
44.7%
intraday143
30.0%
delivery70
 
14.7%
new10
 
2.1%
shares10
 
2.1%
credit10
 
2.1%
from10
 
2.1%
ipo8
 
1.7%
bonus2
 
0.4%

Most occurring characters

ValueCountFrequency (%)
a509
15.7%
r456
14.0%
e383
11.8%
d366
11.3%
t296
9.1%
253
7.8%
y213
6.6%
I151
 
4.7%
n145
 
4.5%
i80
 
2.5%
Other values (16)394
12.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)3246
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a509
15.7%
r456
14.0%
e383
11.8%
d366
11.3%
t296
9.1%
253
7.8%
y213
6.6%
I151
 
4.7%
n145
 
4.5%
i80
 
2.5%
Other values (16)394
12.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3246
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a509
15.7%
r456
14.0%
e383
11.8%
d366
11.3%
t296
9.1%
253
7.8%
y213
6.6%
I151
 
4.7%
n145
 
4.5%
i80
 
2.5%
Other values (16)394
12.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3246
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a509
15.7%
r456
14.0%
e383
11.8%
d366
11.3%
t296
9.1%
253
7.8%
y213
6.6%
I151
 
4.7%
n145
 
4.5%
i80
 
2.5%
Other values (16)394
12.1%

Buy Time
Unsupported

Rejected  Unsupported 

Missing0
Missing (%)0.0%
Memory size3.5 KiB

Sell Time
Unsupported

Rejected  Unsupported 

Missing0
Missing (%)0.0%
Memory size3.5 KiB

Symbol
Categorical

High correlation 

Distinct16
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
Others
63 
INDUSINDBK
35 
RELIANCE
21 
COALINDIA
20 
HINDALCO
10 
Other values (11)
74 

Length

Max length10
Median length9
Mean length7.4977578
Min length3

Characters and Unicode

Total characters1672
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPAYTM
2nd rowIOC
3rd rowCOALINDIA
4th rowOthers
5th rowCOALINDIA

Common Values

ValueCountFrequency (%)
Others63
28.3%
INDUSINDBK35
15.7%
RELIANCE21
 
9.4%
COALINDIA20
 
9.0%
HINDALCO10
 
4.5%
HDFCBANK9
 
4.0%
MOTHERSON9
 
4.0%
PAYTM8
 
3.6%
BERGEPAINT8
 
3.6%
BAJAJHIND7
 
3.1%
Other values (6)33
14.8%

Length

2025-11-08T16:21:19.308687image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
others63
28.3%
indusindbk35
15.7%
reliance21
 
9.4%
coalindia20
 
9.0%
hindalco10
 
4.5%
hdfcbank9
 
4.0%
motherson9
 
4.0%
paytm8
 
3.6%
bergepaint8
 
3.6%
bajajhind7
 
3.1%
Other values (6)33
14.8%

Most occurring characters

ValueCountFrequency (%)
I175
 
10.5%
N172
 
10.3%
A130
 
7.8%
O123
 
7.4%
D120
 
7.2%
B72
 
4.3%
C72
 
4.3%
E71
 
4.2%
L69
 
4.1%
t63
 
3.8%
Other values (16)605
36.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)1672
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
I175
 
10.5%
N172
 
10.3%
A130
 
7.8%
O123
 
7.4%
D120
 
7.2%
B72
 
4.3%
C72
 
4.3%
E71
 
4.2%
L69
 
4.1%
t63
 
3.8%
Other values (16)605
36.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1672
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
I175
 
10.5%
N172
 
10.3%
A130
 
7.8%
O123
 
7.4%
D120
 
7.2%
B72
 
4.3%
C72
 
4.3%
E71
 
4.2%
L69
 
4.1%
t63
 
3.8%
Other values (16)605
36.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1672
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
I175
 
10.5%
N172
 
10.3%
A130
 
7.8%
O123
 
7.4%
D120
 
7.2%
B72
 
4.3%
C72
 
4.3%
E71
 
4.2%
L69
 
4.1%
t63
 
3.8%
Other values (16)605
36.2%

Sector
Categorical

High correlation 

Distinct12
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
Financial Services
66 
Energy
55 
Basic Materials
29 
Technology
16 
Consumer Defensive
12 
Other values (7)
45 

Length

Max length22
Median length18
Mean length13.161435
Min length6

Characters and Unicode

Total characters2935
Distinct characters32
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.4%

Sample

1st rowTechnology
2nd rowEnergy
3rd rowEnergy
4th rowConsumer Cyclical
5th rowEnergy

Common Values

ValueCountFrequency (%)
Financial Services66
29.6%
Energy55
24.7%
Basic Materials29
13.0%
Technology16
 
7.2%
Consumer Defensive12
 
5.4%
Consumer Cyclical11
 
4.9%
Utilities9
 
4.0%
Communication Services8
 
3.6%
Industrials7
 
3.1%
Healthcare5
 
2.2%
Other values (2)5
 
2.2%

Length

2025-11-08T16:21:19.412093image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
services74
21.1%
financial66
18.9%
energy55
15.7%
basic29
 
8.3%
materials29
 
8.3%
consumer23
 
6.6%
technology16
 
4.6%
defensive12
 
3.4%
cyclical11
 
3.1%
utilities9
 
2.6%
Other values (6)26
 
7.4%

Most occurring characters

ValueCountFrequency (%)
i337
11.5%
e332
11.3%
n261
 
8.9%
a257
 
8.8%
c220
 
7.5%
r197
 
6.7%
s195
 
6.6%
l155
 
5.3%
127
 
4.3%
v86
 
2.9%
Other values (22)768
26.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)2935
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i337
11.5%
e332
11.3%
n261
 
8.9%
a257
 
8.8%
c220
 
7.5%
r197
 
6.7%
s195
 
6.6%
l155
 
5.3%
127
 
4.3%
v86
 
2.9%
Other values (22)768
26.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2935
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i337
11.5%
e332
11.3%
n261
 
8.9%
a257
 
8.8%
c220
 
7.5%
r197
 
6.7%
s195
 
6.6%
l155
 
5.3%
127
 
4.3%
v86
 
2.9%
Other values (22)768
26.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2935
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i337
11.5%
e332
11.3%
n261
 
8.9%
a257
 
8.8%
c220
 
7.5%
r197
 
6.7%
s195
 
6.6%
l155
 
5.3%
127
 
4.3%
v86
 
2.9%
Other values (22)768
26.2%

Industry
Categorical

High correlation 

Distinct16
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
Banks - Regional
55 
Others
38 
Oil & Gas Refining & Marketing
28 
Thermal Coal
20 
Aluminum
10 
Other values (11)
72 

Length

Max length40
Median length25
Mean length15.910314
Min length5

Characters and Unicode

Total characters3548
Distinct characters38
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSoftware - Infrastructure
2nd rowOil & Gas Refining & Marketing
3rd rowThermal Coal
4th rowOthers
5th rowThermal Coal

Common Values

ValueCountFrequency (%)
Banks - Regional55
24.7%
Others38
17.0%
Oil & Gas Refining & Marketing28
12.6%
Thermal Coal20
 
9.0%
Aluminum10
 
4.5%
Software - Infrastructure9
 
4.0%
Auto Parts9
 
4.0%
Steel8
 
3.6%
Specialty Chemicals8
 
3.6%
Oil & Gas Integrated7
 
3.1%
Other values (6)31
13.9%

Length

2025-11-08T16:21:19.506864image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
143
23.8%
banks55
 
9.2%
regional55
 
9.2%
others38
 
6.3%
oil35
 
5.8%
gas35
 
5.8%
refining28
 
4.7%
marketing28
 
4.7%
thermal20
 
3.3%
coal20
 
3.3%
Other values (21)144
24.0%

Most occurring characters

ValueCountFrequency (%)
378
 
10.7%
e341
 
9.6%
a283
 
8.0%
i249
 
7.0%
n246
 
6.9%
s189
 
5.3%
r187
 
5.3%
l186
 
5.2%
t178
 
5.0%
g126
 
3.6%
Other values (28)1185
33.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)3548
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
378
 
10.7%
e341
 
9.6%
a283
 
8.0%
i249
 
7.0%
n246
 
6.9%
s189
 
5.3%
r187
 
5.3%
l186
 
5.2%
t178
 
5.0%
g126
 
3.6%
Other values (28)1185
33.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3548
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
378
 
10.7%
e341
 
9.6%
a283
 
8.0%
i249
 
7.0%
n246
 
6.9%
s189
 
5.3%
r187
 
5.3%
l186
 
5.2%
t178
 
5.0%
g126
 
3.6%
Other values (28)1185
33.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3548
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
378
 
10.7%
e341
 
9.6%
a283
 
8.0%
i249
 
7.0%
n246
 
6.9%
s189
 
5.3%
r187
 
5.3%
l186
 
5.2%
t178
 
5.0%
g126
 
3.6%
Other values (28)1185
33.4%
Distinct27
Distinct (%)12.1%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
Minimum2021-11-01 00:00:00
Maximum2025-06-01 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-11-08T16:21:19.581656image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T16:21:19.712606image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
Distinct27
Distinct (%)12.1%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
Minimum2021-11-01 00:00:00
Maximum2025-09-01 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-11-08T16:21:19.819189image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T16:21:19.937101image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=27)

Buy Hour
Real number (ℝ)

High correlation 

Distinct121
Distinct (%)54.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.969581
Minimum9.25
Maximum15.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2025-11-08T16:21:20.041896image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum9.25
5-th percentile9.2833333
Q19.925
median11.733333
Q313.891667
95-th percentile15.481667
Maximum15.5
Range6.25
Interquartile range (IQR)3.9666667

Descriptive statistics

Standard deviation2.1389999
Coefficient of variation (CV)0.17870298
Kurtosis-1.2788063
Mean11.969581
Median Absolute Deviation (MAD)1.9166667
Skewness0.32307033
Sum2669.2167
Variance4.5753205
MonotonicityNot monotonic
2025-11-08T16:21:20.147019image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.2833333339
 
4.0%
9.258
 
3.6%
15.58
 
3.6%
9.3166666675
 
2.2%
15.266666675
 
2.2%
13.45
 
2.2%
9.8166666674
 
1.8%
11.783333334
 
1.8%
15.483333334
 
1.8%
9.5833333334
 
1.8%
Other values (111)167
74.9%
ValueCountFrequency (%)
9.258
3.6%
9.2833333339
4.0%
9.3166666675
2.2%
9.352
 
0.9%
9.3833333331
 
0.4%
9.4166666673
 
1.3%
9.4333333331
 
0.4%
9.4833333331
 
0.4%
9.52
 
0.9%
9.5166666673
 
1.3%
ValueCountFrequency (%)
15.58
3.6%
15.483333334
1.8%
15.466666671
 
0.4%
15.41
 
0.4%
15.366666671
 
0.4%
15.353
 
1.3%
15.333333332
 
0.9%
15.316666672
 
0.9%
15.266666675
2.2%
15.251
 
0.4%

Sell Hour
Real number (ℝ)

High correlation 

Distinct125
Distinct (%)56.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.726607
Minimum9.25
Maximum15.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2025-11-08T16:21:20.291866image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum9.25
5-th percentile9.65
Q111.216667
median12.75
Q314.533333
95-th percentile15.283333
Maximum15.5
Range6.25
Interquartile range (IQR)3.3166667

Descriptive statistics

Standard deviation1.8687079
Coefficient of variation (CV)0.14683473
Kurtosis-1.1905668
Mean12.726607
Median Absolute Deviation (MAD)1.7
Skewness-0.18423732
Sum2838.0333
Variance3.4920692
MonotonicityNot monotonic
2025-11-08T16:21:20.385393image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12.759
 
4.0%
13.94
 
1.8%
15.054
 
1.8%
124
 
1.8%
11.84
 
1.8%
12.766666674
 
1.8%
10.983333334
 
1.8%
9.654
 
1.8%
15.14
 
1.8%
15.166666674
 
1.8%
Other values (115)178
79.8%
ValueCountFrequency (%)
9.251
 
0.4%
9.351
 
0.4%
9.3666666673
1.3%
9.4333333331
 
0.4%
9.451
 
0.4%
9.5333333332
0.9%
9.5666666671
 
0.4%
9.654
1.8%
9.6666666671
 
0.4%
9.7333333332
0.9%
ValueCountFrequency (%)
15.51
 
0.4%
15.416666671
 
0.4%
15.43
1.3%
15.383333332
0.9%
15.316666672
0.9%
15.31
 
0.4%
15.283333333
1.3%
15.251
 
0.4%
15.216666672
0.9%
15.21
 
0.4%

Buy Interval
Categorical

High correlation  Missing 

Distinct7
Distinct (%)3.3%
Missing8
Missing (%)3.6%
Memory size2.3 KiB
09:00–10:00
57 
11:00–12:00
35 
10:00–11:00
32 
15:00–15:30
27 
12:00–13:00
24 
Other values (2)
40 

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters2365
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row09:00–10:00
2nd row10:00–11:00
3rd row09:00–10:00
4th row13:00–14:00
5th row10:00–11:00

Common Values

ValueCountFrequency (%)
09:00–10:0057
25.6%
11:00–12:0035
15.7%
10:00–11:0032
14.3%
15:00–15:3027
12.1%
12:00–13:0024
10.8%
13:00–14:0021
 
9.4%
14:00–15:0019
 
8.5%
(Missing)8
 
3.6%

Length

2025-11-08T16:21:20.477943image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-08T16:21:20.561999image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
09:00–10:0057
26.5%
11:00–12:0035
16.3%
10:00–11:0032
14.9%
15:00–15:3027
12.6%
12:00–13:0024
11.2%
13:00–14:0021
 
9.8%
14:00–15:0019
 
8.8%

Most occurring characters

ValueCountFrequency (%)
0979
41.4%
1440
18.6%
:430
18.2%
215
 
9.1%
573
 
3.1%
372
 
3.0%
259
 
2.5%
957
 
2.4%
440
 
1.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)2365
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0979
41.4%
1440
18.6%
:430
18.2%
215
 
9.1%
573
 
3.1%
372
 
3.0%
259
 
2.5%
957
 
2.4%
440
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2365
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0979
41.4%
1440
18.6%
:430
18.2%
215
 
9.1%
573
 
3.1%
372
 
3.0%
259
 
2.5%
957
 
2.4%
440
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2365
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0979
41.4%
1440
18.6%
:430
18.2%
215
 
9.1%
573
 
3.1%
372
 
3.0%
259
 
2.5%
957
 
2.4%
440
 
1.7%

Sell Interval
Categorical

High correlation 

Distinct7
Distinct (%)3.2%
Missing1
Missing (%)0.4%
Memory size2.3 KiB
15:00–15:30
39 
13:00–14:00
36 
11:00–12:00
34 
12:00–13:00
32 
10:00–11:00
31 
Other values (2)
50 

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters2442
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row09:00–10:00
2nd row11:00–12:00
3rd row13:00–14:00
4th row13:00–14:00
5th row13:00–14:00

Common Values

ValueCountFrequency (%)
15:00–15:3039
17.5%
13:00–14:0036
16.1%
11:00–12:0034
15.2%
12:00–13:0032
14.3%
10:00–11:0031
13.9%
14:00–15:0028
12.6%
09:00–10:0022
9.9%
(Missing)1
 
0.4%

Length

2025-11-08T16:21:20.700913image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-08T16:21:20.788721image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
15:00–15:3039
17.6%
13:00–14:0036
16.2%
11:00–12:0034
15.3%
12:00–13:0032
14.4%
10:00–11:0031
14.0%
14:00–15:0028
12.6%
09:00–10:0022
9.9%

Most occurring characters

ValueCountFrequency (%)
0924
37.8%
1487
19.9%
:444
18.2%
222
 
9.1%
3107
 
4.4%
5106
 
4.3%
266
 
2.7%
464
 
2.6%
922
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)2442
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0924
37.8%
1487
19.9%
:444
18.2%
222
 
9.1%
3107
 
4.4%
5106
 
4.3%
266
 
2.7%
464
 
2.6%
922
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2442
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0924
37.8%
1487
19.9%
:444
18.2%
222
 
9.1%
3107
 
4.4%
5106
 
4.3%
266
 
2.7%
464
 
2.6%
922
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2442
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0924
37.8%
1487
19.9%
:444
18.2%
222
 
9.1%
3107
 
4.4%
5106
 
4.3%
266
 
2.7%
464
 
2.6%
922
 
0.9%

Interactions

2025-11-08T16:21:15.035467image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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Correlations

2025-11-08T16:21:20.941265image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Buy HourBuy IntervalBuy priceBuy valueIndustryQuantityRealised P&LRemarkSectorSell HourSell IntervalSell priceSell valueSymbol
Buy Hour1.0000.861-0.139-0.0780.324-0.004-0.0370.2880.2060.2460.196-0.164-0.1000.317
Buy Interval0.8611.0000.1400.1460.3060.0000.0000.2370.2220.2230.1900.1310.1730.291
Buy price-0.1390.1401.0000.6970.502-0.201-0.3000.1790.439-0.1310.0600.9730.6680.535
Buy value-0.0780.1460.6971.0000.2540.514-0.2070.1960.184-0.1380.0340.6740.9860.306
Industry0.3240.3060.5020.2541.0000.0000.0000.2540.7590.2440.2700.5330.2550.830
Quantity-0.0040.000-0.2010.5140.0001.0000.0110.4000.143-0.0410.041-0.2130.5280.000
Realised P&L-0.0370.000-0.300-0.2070.0000.0111.0000.6180.190-0.0260.113-0.212-0.1390.000
Remark0.2880.2370.1790.1960.2540.4000.6181.0000.1950.2710.1450.2170.3140.281
Sector0.2060.2220.4390.1840.7590.1430.1900.1951.0000.1840.1820.4120.2260.628
Sell Hour0.2460.223-0.131-0.1380.244-0.041-0.0260.2710.1841.0000.891-0.152-0.1460.264
Sell Interval0.1960.1900.0600.0340.2700.0410.1130.1450.1820.8911.0000.1320.0940.263
Sell price-0.1640.1310.9730.6740.533-0.213-0.2120.2170.412-0.1520.1321.0000.6770.571
Sell value-0.1000.1730.6680.9860.2550.528-0.1390.3140.226-0.1460.0940.6771.0000.297
Symbol0.3170.2910.5350.3060.8300.0000.0000.2810.6280.2640.2630.5710.2971.000

Missing values

2025-11-08T16:21:15.597402image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-11-08T16:21:15.735534image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-11-08T16:21:15.851910image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Stock nameISINQuantityBuy dateBuy priceBuy valueSell dateSell priceSell valueRealised P&LRemarkBuy TimeSell TimeSymbolSectorIndustryBuy MonthSell MonthBuy HourSell HourBuy IntervalSell Interval
0ONE 97 COMMUNICATIONS LTDINE982J0102012021-11-251833.551833.552021-11-251823.001823.00-10.55Intraday trade09:23:0009:27:00PAYTMTechnologySoftware - InfrastructureNov-2021Nov-20219.3833339.45000009:00–10:0009:00–10:00
1INDIAN OIL CORP LTDINE242A01010102022-05-30114.401144.002023-05-1886.85868.50-275.50Delivery Trade10:29:0011:48:00IOCEnergyOil & Gas Refining & MarketingMay-2022May-202310.48333311.80000010:00–11:0011:00–12:00
2COAL INDIA LTDINE522F0101412022-06-02195.50195.502022-08-08215.00215.0019.50Delivery Trade09:34:0013:54:00COALINDIAEnergyThermal CoalJun-2022Aug-20229.56666713.90000009:00–10:0013:00–14:00
3ETERNAL LIMITEDINE758T0101512022-06-0372.4572.452022-06-0372.6072.600.15Intraday trade13:12:0013:07:00OthersConsumer CyclicalOthersJun-2022Jun-202213.20000013.11666713:00–14:0013:00–14:00
4COAL INDIA LTDINE522F0101412022-06-03196.50196.502022-08-08215.00215.0018.50Delivery Trade10:32:0013:54:00COALINDIAEnergyThermal CoalJun-2022Aug-202210.53333313.90000010:00–11:0013:00–14:00
5INDUSIND BANK LIMITEDINE095A0101252022-06-08921.504607.502022-08-051036.455182.25574.75Delivery Trade10:13:0015:23:00INDUSINDBKFinancial ServicesBanks - RegionalJun-2022Aug-202210.21666715.38333310:00–11:0015:00–15:30
6COAL INDIA LTDINE522F0101452022-06-09198.65993.252022-06-09200.051000.257.00Intraday trade15:11:0010:24:00COALINDIAEnergyThermal CoalJun-2022Jun-202215.18333310.40000015:00–15:3010:00–11:00
7INDUSIND BANK LIMITEDINE095A0101222022-06-14861.501723.002022-06-14857.651715.30-7.70Intraday trade10:34:0010:41:00INDUSINDBKFinancial ServicesBanks - RegionalJun-2022Jun-202210.56666710.68333310:00–11:0010:00–11:00
8INDUSIND BANK LIMITEDINE095A0101232022-06-14855.002565.002022-06-14857.652572.957.95Intraday trade10:34:0010:41:00INDUSINDBKFinancial ServicesBanks - RegionalJun-2022Jun-202210.56666710.68333310:00–11:0010:00–11:00
9COAL INDIA LTDINE522F0101452022-06-14192.50962.502022-06-14192.40962.00-0.50Intraday trade09:17:0010:51:00COALINDIAEnergyThermal CoalJun-2022Jun-20229.28333310.85000009:00–10:0010:00–11:00
Stock nameISINQuantityBuy dateBuy priceBuy valueSell dateSell priceSell valueRealised P&LRemarkBuy TimeSell TimeSymbolSectorIndustryBuy MonthSell MonthBuy HourSell HourBuy IntervalSell Interval
214NTPC LTDINE733E0101052024-09-23429.702148.502024-11-28362.301811.50-337.00Delivery Trade10:42:0014:16:00OthersUtilitiesUtilities - Regulated ElectricSep-2024Nov-202410.70000014.26666710:00–11:0014:00–15:00
215GLENMARK PHARMACEUTICALSINE935A0103552024-09-271689.408447.002024-09-271687.658438.25-8.75Intraday trade11:53:0012:20:00OthersHealthcareDrug Manufacturers - Specialty & GenericSep-2024Sep-202411.88333312.33333311:00–12:0012:00–13:00
216NTPC GREEN ENERGY LIMITEDINE0ONG010111002024-11-26108.0010800.002024-11-29126.7612676.001876.00New shares credit from IPO15:30:0011:56:00OthersUtilitiesOthersNov-2024Nov-202415.50000011.933333NaN11:00–12:00
217NTPC GREEN ENERGY LIMITEDINE0ONG01011382024-11-26108.004104.002024-11-29126.354801.30697.30New shares credit from IPO15:30:0011:56:00OthersUtilitiesOthersNov-2024Nov-202415.50000011.933333NaN11:00–12:00
218REDINGTON LIMITEDINE891D01026202025-02-13243.904878.002025-08-18237.754755.00-123.00Delivery Trade14:43:0010:43:00OthersTechnologyOthersFeb-2025Aug-202514.71666710.71666714:00–15:0010:00–11:00
219POWER FIN CORP LTD.INE134E01011252025-03-24424.3010607.502025-06-03406.0510151.25-456.25Delivery Trade15:05:0012:26:00OthersFinancial ServicesCredit ServicesMar-2025Jun-202515.08333312.43333315:00–15:3012:00–13:00
220MIRAEAMC - MAFSETFINF769K01HI8502025-03-2425.751287.502025-08-1827.641382.0094.50Delivery Trade15:21:0010:46:00OthersOthersOthersMar-2025Aug-202515.35000010.76666715:00–15:3010:00–11:00
221BLACK BOX LIMITEDINE676A01027252025-03-24370.659266.252025-09-17489.4512236.252970.00Delivery Trade15:24:0015:11:00OthersTechnologyOthersMar-2025Sep-202515.40000015.18333315:00–15:3015:00–15:30
222KPI GREEN ENERGY LIMITEDINE542W01025102025-06-11539.235392.302025-08-18523.355233.50-158.80Delivery Trade12:31:0010:39:00OthersUtilitiesOthersJun-2025Aug-202512.51666710.65000012:00–13:0010:00–11:00
223BHARAT PETROLEUM CORP LTINE029A01011142025-06-11333.454668.302025-08-18315.104411.40-256.90Delivery Trade12:33:0010:44:00OthersEnergyOil & Gas Refining & MarketingJun-2025Aug-202512.55000010.73333312:00–13:0010:00–11:00